Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "111" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 26 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 26 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460009 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 9.350289 | 15.050850 | 0.639369 | 13.717991 | 5.562965 | 8.852556 | 42.296063 | 2.867111 | 0.5602 | 0.0642 | 0.4158 | nan | nan |
| 2460008 | digital_ok | 100.00% | 0.00% | 96.92% | 0.00% | - | - | 39.220689 | 18.397062 | 1.743373 | 15.090011 | 3.010867 | 7.785815 | 2.442355 | 5.657914 | 0.5333 | 0.0829 | 0.3288 | nan | nan |
| 2460007 | digital_ok | 100.00% | 0.00% | 99.89% | 0.00% | - | - | 29.515473 | 13.752625 | 1.412460 | 11.797773 | 3.465357 | 7.226208 | 7.269946 | 3.055323 | 0.4875 | 0.0657 | 0.3188 | nan | nan |
| 2459999 | digital_ok | 0.00% | 98.91% | 99.08% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.2624 | 0.2306 | 0.2194 | nan | nan |
| 2459998 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 28.544985 | 11.650891 | 1.290522 | 9.982610 | 1.467061 | 10.242129 | 3.072837 | 2.313136 | 0.4748 | 0.0576 | 0.3184 | nan | nan |
| 2459997 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 31.010530 | 12.693897 | 1.279156 | 10.732182 | 1.447509 | 9.652016 | 3.966135 | 3.937303 | 0.4917 | 0.0653 | 0.3224 | nan | nan |
| 2459996 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 32.269730 | 13.710123 | 1.959634 | 13.146863 | 1.102406 | 9.281582 | 3.316202 | 1.700617 | 0.4886 | 0.0627 | 0.3318 | nan | nan |
| 2459995 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 34.296875 | 13.865448 | 1.318695 | 12.339438 | 1.816382 | 9.523638 | 4.129357 | 1.359815 | 0.4955 | 0.0674 | 0.3306 | nan | nan |
| 2459994 | digital_ok | 100.00% | 0.00% | 99.57% | 0.00% | - | - | 34.150538 | 13.454299 | 1.152565 | 10.825094 | 1.755214 | 9.575229 | 3.974764 | 1.873463 | 0.4885 | 0.0604 | 0.3336 | nan | nan |
| 2459993 | digital_ok | 100.00% | 0.00% | 98.11% | 0.00% | - | - | 39.108023 | 12.669397 | 0.747553 | 10.007154 | 5.762453 | 10.935918 | 8.903885 | 2.594276 | 0.4697 | 0.0464 | 0.3238 | nan | nan |
| 2459991 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 39.860222 | 15.673635 | 1.083426 | 10.595305 | 4.615899 | 10.774775 | 7.784303 | 1.074023 | 0.4832 | 0.0563 | 0.3282 | nan | nan |
| 2459990 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 31.683956 | 12.896195 | 0.993650 | 10.288313 | 5.329859 | 11.068743 | 18.612296 | 1.039106 | 0.4881 | 0.0582 | 0.3306 | nan | nan |
| 2459989 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 32.319087 | 13.078447 | 0.778966 | 9.409693 | 4.832818 | 9.285545 | 7.611176 | 0.607436 | 0.4917 | 0.0546 | 0.3385 | nan | nan |
| 2459988 | digital_ok | 100.00% | 0.00% | 99.84% | 0.00% | - | - | 39.883836 | 15.304054 | 0.951485 | 10.573462 | 3.556297 | 13.233975 | 7.451913 | 0.888676 | 0.4881 | 0.0575 | 0.3246 | nan | nan |
| 2459987 | digital_ok | 100.00% | 0.00% | 98.00% | 0.00% | - | - | 32.973823 | 12.785210 | 1.080876 | 10.443937 | 1.292093 | 7.984577 | 1.551138 | 2.371465 | 0.4882 | 0.0683 | 0.3184 | nan | nan |
| 2459986 | digital_ok | 100.00% | 0.00% | 97.52% | 0.00% | - | - | 40.012680 | 15.759733 | 1.188032 | 11.289027 | 2.791294 | 11.273826 | 2.890096 | 9.847831 | 0.5155 | 0.0805 | 0.3232 | nan | nan |
| 2459985 | digital_ok | 100.00% | 0.00% | 99.95% | 0.00% | - | - | 36.948314 | 14.227802 | 1.207240 | 10.502703 | 1.200186 | 8.620191 | -0.086699 | 2.479065 | 0.4862 | 0.0618 | 0.3175 | nan | nan |
| 2459984 | digital_ok | 100.00% | 0.00% | 96.92% | 0.00% | - | - | 33.802738 | 13.670705 | 1.419952 | 10.885485 | 2.035136 | 12.064654 | -0.029231 | 3.225242 | 0.5071 | 0.0823 | 0.3154 | nan | nan |
| 2459983 | digital_ok | 100.00% | 21.93% | 100.00% | 0.00% | - | - | 41.752795 | 13.510041 | 1.735032 | 10.292621 | 4.416704 | 11.116559 | 1.814702 | 6.471497 | 0.2829 | 0.0611 | 0.1441 | nan | nan |
| 2459982 | digital_ok | 100.00% | 0.00% | 87.30% | 0.00% | - | - | 22.800352 | 10.567661 | 1.645058 | 8.671700 | 2.092761 | 5.056369 | 0.877748 | 3.135462 | 0.4020 | 0.1248 | 0.2169 | nan | nan |
| 2459981 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 27.135943 | 12.517518 | 2.813737 | 10.783914 | 5.262107 | 11.976767 | 1.615584 | 1.280316 | 0.3019 | 0.0847 | 0.1730 | nan | nan |
| 2459980 | digital_ok | 100.00% | 0.00% | 91.68% | 0.00% | - | - | 26.348750 | 12.060877 | 2.480850 | 9.856624 | 5.124084 | 10.424646 | 2.945676 | 5.137114 | 0.3600 | 0.1102 | 0.1923 | nan | nan |
| 2459979 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 27.879608 | 12.582020 | 2.244326 | 9.223745 | 4.691529 | 9.824220 | 2.043451 | 1.085674 | 0.2965 | 0.0771 | 0.1741 | nan | nan |
| 2459978 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 28.572652 | 12.837238 | 2.557157 | 9.932125 | 4.529721 | 10.661595 | 2.186622 | 1.274169 | 0.2876 | 0.0745 | 0.1708 | nan | nan |
| 2459977 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 29.623255 | 13.636599 | 1.103260 | 9.786906 | 7.283141 | 10.868877 | 2.287414 | 1.493878 | 0.2956 | 0.0843 | 0.1626 | nan | nan |
| 2459976 | digital_ok | 100.00% | 0.00% | 79.08% | 0.00% | - | - | -0.040457 | 11.883499 | -0.499553 | 10.172576 | -0.425421 | 10.184482 | 1.924558 | 1.457278 | 0.6398 | 0.1769 | 0.4661 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Temporal Discontinuties | 42.296063 | 9.350289 | 15.050850 | 0.639369 | 13.717991 | 5.562965 | 8.852556 | 42.296063 | 2.867111 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 39.220689 | 18.397062 | 39.220689 | 15.090011 | 1.743373 | 7.785815 | 3.010867 | 5.657914 | 2.442355 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 29.515473 | 29.515473 | 13.752625 | 1.412460 | 11.797773 | 3.465357 | 7.226208 | 7.269946 | 3.055323 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 28.544985 | 28.544985 | 11.650891 | 1.290522 | 9.982610 | 1.467061 | 10.242129 | 3.072837 | 2.313136 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 31.010530 | 31.010530 | 12.693897 | 1.279156 | 10.732182 | 1.447509 | 9.652016 | 3.966135 | 3.937303 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 32.269730 | 32.269730 | 13.710123 | 1.959634 | 13.146863 | 1.102406 | 9.281582 | 3.316202 | 1.700617 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 34.296875 | 34.296875 | 13.865448 | 1.318695 | 12.339438 | 1.816382 | 9.523638 | 4.129357 | 1.359815 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 34.150538 | 34.150538 | 13.454299 | 1.152565 | 10.825094 | 1.755214 | 9.575229 | 3.974764 | 1.873463 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 39.108023 | 39.108023 | 12.669397 | 0.747553 | 10.007154 | 5.762453 | 10.935918 | 8.903885 | 2.594276 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 39.860222 | 39.860222 | 15.673635 | 1.083426 | 10.595305 | 4.615899 | 10.774775 | 7.784303 | 1.074023 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 31.683956 | 12.896195 | 31.683956 | 10.288313 | 0.993650 | 11.068743 | 5.329859 | 1.039106 | 18.612296 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 32.319087 | 13.078447 | 32.319087 | 9.409693 | 0.778966 | 9.285545 | 4.832818 | 0.607436 | 7.611176 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 39.883836 | 15.304054 | 39.883836 | 10.573462 | 0.951485 | 13.233975 | 3.556297 | 0.888676 | 7.451913 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 32.973823 | 32.973823 | 12.785210 | 1.080876 | 10.443937 | 1.292093 | 7.984577 | 1.551138 | 2.371465 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 40.012680 | 15.759733 | 40.012680 | 11.289027 | 1.188032 | 11.273826 | 2.791294 | 9.847831 | 2.890096 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 36.948314 | 14.227802 | 36.948314 | 10.502703 | 1.207240 | 8.620191 | 1.200186 | 2.479065 | -0.086699 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 33.802738 | 33.802738 | 13.670705 | 1.419952 | 10.885485 | 2.035136 | 12.064654 | -0.029231 | 3.225242 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 41.752795 | 41.752795 | 13.510041 | 1.735032 | 10.292621 | 4.416704 | 11.116559 | 1.814702 | 6.471497 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 22.800352 | 22.800352 | 10.567661 | 1.645058 | 8.671700 | 2.092761 | 5.056369 | 0.877748 | 3.135462 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 27.135943 | 12.517518 | 27.135943 | 10.783914 | 2.813737 | 11.976767 | 5.262107 | 1.280316 | 1.615584 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 26.348750 | 12.060877 | 26.348750 | 9.856624 | 2.480850 | 10.424646 | 5.124084 | 5.137114 | 2.945676 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 27.879608 | 27.879608 | 12.582020 | 2.244326 | 9.223745 | 4.691529 | 9.824220 | 2.043451 | 1.085674 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 28.572652 | 12.837238 | 28.572652 | 9.932125 | 2.557157 | 10.661595 | 4.529721 | 1.274169 | 2.186622 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | ee Shape | 29.623255 | 29.623255 | 13.636599 | 1.103260 | 9.786906 | 7.283141 | 10.868877 | 2.287414 | 1.493878 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111 | N10 | digital_ok | nn Shape | 11.883499 | 11.883499 | -0.040457 | 10.172576 | -0.499553 | 10.184482 | -0.425421 | 1.457278 | 1.924558 |